We report on two recirculating loop transmission experiments over a hollow-core fiber of the Nested-Antiresonant Nodeless type (NANF). We transmitted 61 channels in C-band at 32 GBaud, with either PM-QPSK or PM-16QAM modulation. In addition, 61 L-band interferers co-propagated in the NANF at all times, though they were not recirculated in the loop, to check for the presence of possible crosstalk effects between C and L-band in the NANF. The loop comprised the longest NANF transmission line yet constructed (7.72 km), as well as 55 km of PSCF needed to provide enough signal buffering and EDFA stabilization for the loop. The launch power into the PSCF was low enough to avoid generation of any significant non-linear noise. Using PM-QPSK, we achieved a record 618 km transmission in NANF (80 recirculations), at an overall average GMI of 3.44 bits/symb. Using PM-16QAM, we achieved a record 201 km transmission, at an overall average GMI of 7 bits/symb. We saw no adverse effect from the presence of L-band interferers in the NANF. If progress in the reduction of NANF loss and inter-modal interference continues at the rate of the last few years, these hollow-core fibers might become a promising alternative in the quest for next-generation higher-throughput fibers, given their theoretical potential of achieving low loss and ultra-low nonlinearity over ultra-wide bandwidths, ideally bringing about a many-fold increase in throughput per fiber.

Optical communications and networking are fast becoming the solution to support ever-increasing data traffic across all segments of the network, expanding from core/metro networks to 5G/6G front-hauling. Therefore, optical networks need to evolve towards an efficient exploitation of the infrastructure by overcoming the closed and aggregated paradigm, to enable apparatus sharing together with the slicing and separation of the optical data plane from the optical control. In addition to the advantages in terms of efficiency and cost reduction, this evolution will increase network reliability, also allowing for a fine trade-off between robustness and maximum capacity exploitation. In this work, an optical network architecture is presented based on the physical layer digital twin of the optical transport used within a multi-layer hierarchical control operated by an intent-based network operating system. An experimental proof of concept is performed on a three-node network including up to 1000 km optical transmission, open re-configurable optical add & drop multiplexers (ROADMs) and whitebox transponders hosting pluggable multirate transceivers. The proposed solution is based on GNPy as the optical physical layer digital twin and ONOS as intent-based network operating system. The reliability of the optical control decoupled by the data plane functioning is experimentally demonstrated exploiting GNPy as open lightpath computation engine and software optical amplifier models derived from the component characterization. Besides the lightpath deployment exploiting the modulation format evaluation given a generic traffic request, the architecture reliability is tested mimicking the use case of an automatic failure recovery from a fiber cut.

This paper focuses on a closed-form ultra-wide-band solution of the GN and EGN model integrals (CFM, for closed form model), collaboratively developed by CISCO and Politecnico di Torino. The version presented here is capable of handling the frequency-dependence of all main system and fiber parameters and accounts accurately for Interchannel Stimulated Raman Scattering (ISRS). In the paper, we first describe the CFM in detail and then we present a validation experiment carried out over a 5-span 128-channel C+L transmission line over SMF. The experiment was designed to explore transmission scenarios ranging from quasi-linear to deeply non-linear, with significant ISRS. We describe the thorough characterization of the link and the careful instrumentation of the set-up, necessary to minimize all errors that might compromise the significance of the measurements. The results are extensively discussed and show a good match between the CFM predictions and the measured performance. The CFM provides a closed-form solution of the GN and EGN model integrals, but other steps of system performance assessment are not closed-form. However, since GN/EGN integrals are by far the most computationally heavy element of the calculation, the overall speed-up granted by using the CFM is quite significant, about four orders of magnitude. The real-time system performance assessment capability thus achieved makes it possible to perform physical-layer-aware numerical optimization of system and link parameters, a critical aspect in multi-band and ultra-wide-band optical networks. Overall, we believe that both the speed and the reliability of the CFM make it a valuable tool with significant potential for practical application in the field.

Modern intra-data center (IDC) interconnects leverage robust and low-cost intensity modulation (IM) and direct detection (DD) optical links, based on multimode fibers (MMFs) and vertical-cavity surface-emitting lasers (VCSELs). Current solutions, based on on-off keying (OOK) modulations, reach up to 25-50 Gbps per lane over nearly 100 meters. The actual target for IDCs is to increase VCSEL-MMF links capacity up to 100 Gbps, using PAM-4 on the same devices. To counteract the consequent linear and nonlinear distortions affecting the transmitted signals, an effective solution is to exploit digital signal processing (DSP). In this manuscript, we propose a novel method to optimize a nonlinear artificial neural network (ANN) digital pre-distorter (DPD), based on End-to-end (E2E) learning, that, trained jointly with a Feed-Forward Equalizer (FFE), fulfills physical amplitude constraints and handles different ratio between the sampling rates incurring along with an optical IM-DD system. We indeed propose an E2E ANN system operating simultaneously at different sampling frequencies. We moreover propose in our training method a substitution to the time-domain injection of the receiver noise in the system with an additive regularization term in the FFE gradient loss. We experimentally show the advantages of our proposed DPD comparing the bit error rate (BER) performance against the same scenario without DPD. We assess the gain in terms of Gross Bit Rate and Optical Path Loss (OPL), at given BER targets, for different fiber lengths.

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes’ destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas.

We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical disturbances, and (iii) malicious fiber tapping (eavesdropping). We used various supervised machine learning techniques like k-Nearest Neighbor (k-NN), random forest, extreme gradient boosting (XGBoost), and decision trees to classify different vibration events. We also assessed the framework’s resilience to background interference by superimposing sinusoidal noise at different frequencies and examining its effects on the polarization signatures. This analysis provides insight into how subsurface installations, subject to ambient vibrations, affect detection fidelity. This highlights the sensitivity to which external interference affects polarization fingerprints. Crucially, it demonstrates the system’s capacity to discern and alert on malicious vibration events even in the presence of environmental noise. However, we focus on the necessity of noise-mitigation techniques in real-world implementations while providing a potent, real-time mechanism for multi-threat recognition in the fiber networks.